Customized action based on video item events

ABSTRACT

A user may indicate an interest relating to events such as objects, persons, or activities, where the events included in content depicted in a video. The user may also indicate a configurable action associated with the user interest, including receiving a notification via an electronic device. A video item, for example a live-streaming sporting event, may be broken into frames and analyzed frame-by-frame to determine a region of interest. The region of interest is then analyzed to identify objects, persons, or activities depicted in the frame. In particular, the region of interest is compared to stored images that are known to depict different objects, persons, or activities. When a region of interest is determined to be associated with the user interest, the configurable action is triggered.

BACKGROUND

A large and growing population of users enjoy entertainment throughconsumption of video items. “Video items” and “video content,” as usedherein, include television shows, sporting events, concerts, movies,documentaries, and the like. Video content available to users includespreviously-recorded video content and live, or near-live (also referredto herein as real-time or near real-time), video content. Traditionally,video content was accessible through a limited number oftelecommunication mediums (e.g., television sets). Many people todayconsume video content through a wide variety of electronic devices.Among these electronic devices include cellular telephones, tabletcomputing devices, digital media players, laptop computers, desktopcomputers, television, virtual reality devices, and the like. Videocontent is available through a variety of services including broadcastchannels via antenna and/or satellite, cable channels, cable andsatellite replacement services, subscription and non-subscriptionstreaming services, websites, mobile applications, pay-per-viewservices, and the like.

As more video content is made available to users through a growingnumber of electronic devices and services, there is a need foradvancement with respect to user experience when consuming videocontent, including live video content. There is also a need to provideusers with systems and processes to facilitate configurable consumptionof video content.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is set forth with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical items or features.

FIG. 1 illustrates an example system for triggering an action based onidentifying an event in a video item.

FIG. 2 illustrates an example system that includes multiple devices thatfacilitate the triggering of an action based on identifying an event ina video item.

FIG. 3 is a diagram of an illustrative process to determine region(s) ofinterest with respect to a frame of a video item.

FIG. 4 is a diagram of an illustrative process to analyze region(s) ofinterest with respect to a database.

FIG. 5 is a diagram an illustrative process to analyze region(s) ofinterest with respect to a video item.

FIG. 6 is a flow diagram illustrating an example process of determininginputs and outputs of a machine learning model.

FIG. 7 is a flow diagram illustrating an example process of determiningan additional action based on identifying an event in a video item.

FIG. 8 is a flow diagram illustrating an example process of triggeringan action based on identifying an event in a video item.

DETAILED DESCRIPTION

In the context of video items, including video content, accessible tousers via electronic devices, there are challenges providingcustomizable consumption options for individual users. For instance,users often spend large amounts of time sifting through available videocontent offered via an increasing number of services. With respect toconsuming video items, users may seek to improve their user experience.Users may wish to select, or be recommended, configurable actions toboth supplement their ability to find suitable programming and tosupplement a viewing experience. In addition, users may wish to stayup-to-date on objects, persons, and activities included in video contentwhen they are away from their electronic devices (e.g., receive updateson particular actresses or particular athletes).

For instance, a user may indicate in their user settings their love ofthe show “Jeopardy.” The user, interested in playing along with thecontestants in the show, may desire a configurable action to betriggered in the event the host of show, Alex Trebek, appears on screen.The configurable action may be to increase the audio-volume by apredetermined amount of their television set when Alex Trebek appears onscreen to read questions and to return the audio-volume to thepre-increased level when Alex Trebek is off-screen. In this way, theuser may be able to better hear the questions from the host. Inaddition, the user may better attempt to answer the question when thehost is off-screen because the contestants' answers are presented with arelatively lower audio-volume and therefore may not be influenced by thecontestants' answers. By being able to parse the meaning of videocontent, there are numerous opportunities to leverage this meaning totrigger configurable actions to improve a user's experience whenconsuming a video item (e.g., increasing and decreasing audio volume ofa television show). Based on the quantity and variety of video contentavailable for consumption by users, the ability to provide customizablesolutions for a user allows users to become a more active participantduring their video item consumption.

Specifically, in the context of live-video content, there are additionalchallenges to determining the events included in the video content inreal-time such that the determined events may be leveraged to triggerconfigurable actions. For instance, during a live presentation of abaseball game there are events with respect to the various objects,players, and activities that occur during the course of the game. Withrespect to objects: uniforms, baseballs, bats, helmets, cleats, bases,base paths, foul poles, wind-direction flags, and in-game video monitorsare presented during the course of the game. With respect to players:particular players, coaches, and/or umpires participate in the game.With respect to actions: hits (e.g., singles, doubles, triples, andhome-runs), pitches (e.g., strikes, balls, strikeouts, walks, fastballs,curveballs, sliders, cutters, and knuckleballs), and other miscellaneousactions (e.g., stolen bases, runners caught stealing bases, pinch orreplacement hitters and pitchers), occur during presentation of thevideo content.

Further, in the context of professional baseball, these various objects,players, and activities occur during each game for every team. Baseballfans, even the most enthusiastic supporters, are tasked with keepingtabs on the hundreds of players and the thousands of professionalbaseball games that are played each year. For instance, a person may beunable to keep track of their favorite players and teams. In someinstances, manual extracting may be performed to provide users withalerts to tune-into a specific game. However, these alerts are oftengeneralized to users (e.g., scoring play alerts, game start times) andrequire human intervention, at least partially, to be performed.

This problem is exacerbated if a user is also interested in keeping upwith multiple types of live video content (e.g., sports, current events,politics, or concerts) and/or pre-recorded video content (e.g.,television shows, movies, or documentaries). The user, wishing to keeptrack of particular events included in video content, are faced with anoverwhelming quantity and variety of video content to sift through.Further, the user may also wish to have customized actions (e.g.,notifications via an e-mail message, short message service (SMS)message, and/or multimedia messaging service (MMS) message) occur when aparticular event occurs. For instance, when a particular baseball playerenters the game (or is up to bat), the user may wish to receive a textmessage notification alerting them of the event. A user may also desireto customize what events trigger the actions (e.g., notifications) astheir interests shift.

These challenges represent a technological problem because they arebased on an inability to analyze, extract, and determine events in videocontent. The inability to capture events included in video content,including in live or near-live video content, is a result of aninability to mimic human semantic understanding by a machine.Accordingly, there is currently a missed opportunity to associatesemantic understanding of video content, events, to trigger actions(e.g., notifications) that could improve user experience.

Traditionally, as video content is presented and/or ran, events includedin the video content were extracted manually. For instance, in thecontext of a baseball game, as the events of the game are occurringlive, a human would watch the game and manually extract and/or recordthe events of the game (e.g., runs scored, player changes). The manualextraction occurred because of an inability for a computer to understandthe content of the video data. Manual extraction was needed for semanticunderstanding of nuanced situations included in video content (e.g., asequence of actions during baseball game). Computers lacked the abilityto understand when events were occurring in video content, especially inreal-time. Further, this extraction occurred at a generalized level asopposed to at an individual user level. In addition, after manuallyextracting events from video data, there were challenges associatingevents with triggers to complete user facing actions. These shortcomingsare at least a result of insufficient systems and processes to determineevents in video content and to trigger actions accordingly.

The systems and processes described herein provide techniques fordetermining events (e.g., objects, persons/faces, and activities)included in video content. In addition, techniques for associating andtriggering user-facing actions based on determining the events aredescribed herein. The techniques are directed to solving the problem ofan inability for a computer to understand events included in videoitems, and specifically, in video content. These techniques can beperformed in real-time. For instance, these techniques can be applied tolive or near-live video items.

The techniques herein are also directed to solving the problem ofassociating events included within video content to customizable,user-facing actions. In various embodiments, these techniques improvethe functionality of electronic devices. User-facing electronic devicefunctionality may be modified as a result of determining an eventincluded in the video content. For instance, in response to determininga particular player has entered a baseball game (i.e., an event), atelevision set may increase the audio volume by a configurable level(i.e., an action).

Additionally, the techniques herein enable a computer to performfunctions that the computer could previously not perform. Traditionalmethods to solve challenges with extracting events included in a videorelated to manual extraction. Further, manual extraction wastraditionally generalized event detection rather than tailored toindividual user interests. In addition, generalized event detection wasused in conjunction with generalized user-facing actions. In addition,these traditional methods represented a considerable burden on acomputer network. Manual extraction requires considerable electronic andhuman resources to present and consume video content for extractingsemantic understanding. In addition, this labor intensive process wasoften completed using associated networks.

In various embodiments, users may view and interact with content via anelectronic device. These electronic devices include cellular telephones,tablet computing devices, digital media players, laptop computers,desktop computers, television, virtual reality devices, and the like. Invarious embodiments, a user may access a site that displays or presentsa plurality of video items. A user may also consume video items throughbroadcast channels via antenna and/or satellite, cable channels, cableand satellite replacement services, subscription and non-subscriptionstreaming services, websites, mobile applications, pay-per-viewservices, and the like. A video item may be created by a serviceprovider or may be received from individuals or entities that author,create, broadcast, or facilitate presentation of the video item. Theuser may consume the video item via the electronic device.

Techniques are disclosed herein to determine an event included in avideo item and to associate the event with a user-facing action. A framecan be determined from a video item and/or video item portion. A videoitem can be broken up into individual frames. A frame may include animage representing a time period, interval, and or/instance of the videoitem. A video item can be divided into a predetermined number of frames.In various embodiments, the number of frames can be based onpredetermined time periods. For instance, one frame may be an imagerepresenting one second of a video. The number of frames representingthe video item may be a single frame. In various embodiments, the numberof frames may be based on the frame rate of the video item and/or thenumber of frames may be based on varying time periods in the video item.For instance, a first video item portion may represent one frame per onesecond of the video. A second video item portion may represent one frameper three seconds of the video item. A third video item portion may berepresented by a single frame. Therefore, the video item may be brokenup according to a fixed time interval or a variable time interval. Forinstance, a 60 second, live-video may be broken into frames. Each framemay be an image representing one second of the live-video. Therefore,the live-video would be broken into 60 frames. In various embodiments, avideo item may be previously broken into frames and received by thecontent servers. In some instances, timestamp data may be extracted,determined, and/or associated with different video item portions and/orframes.

In various embodiments, a user interest associated with an event (e.g.,an object, a person, and/or an activity) they are interested infollowing may be received and/or determined. In various embodiments, theservice provider and/or content servers may receive or determine a userinterest. In some instances, a user may indicate they are interested infollowing a particular person. For example, the user may indicate theyare interested in the following: her favorite baseball players(persons); any player in that is playing wearing a particular type ofshoe (objects); and any Major League Baseball player that hits a homerun (activities). In various embodiments, user interests may beassociated with user settings and/or user preferences. In variousembodiments, a user may indicate a user interest via an electronicdevice. In some instances, a user may select options from predefinedfields or provide comments. In any event, user interests of a user maybe stored in association with a user profile or user account that isassociated with the user and that is possibly maintained by a serviceprovider that offers video items for consumption.

In various embodiments, a user interest may be system generated,determined, and/or received by the content servers and/or serviceprovider. In some instances, a user interest may be recommended to theuser via the service provider. For instance, based on user settings,user preferences, previous purchases, previously consumed video items,and/or user information, and user interests can be recommended to theuser. In some instances, a user may be included in a user group withadditional users with similar interests. The user group may receiverecommendations recommending a potential interest to the group. Forinstance, a user may be included in a “Seattle Sports” user group basedon their user settings. The “Seattle Sports” user group may beautomatically provided with a recommendation to follow particularplayers on sports teams located in or around Seattle, Wash.

In various embodiments, a user interest associated with an event mayindicate an action to associate with the events (e.g., objects, person,and/or events) they are interested in following. In some instances, theaction selection may include receiving a notification, receiving anshort message service (SMS) message, receiving a multimedia messagingservice (MMS) message, receiving an e-mail message, causing the videoitem to be presented via an electronic device, causing an electronicdevice to be powered on and/or off, causing an electronic device toincrease and/or decrease in audio volume, recording a video item orvideo item portion, causing an electronic device to identify a secondelectronic device, causing an electronic device to be communicativelycoupled to a second electronic device, causing a summary of the event tobe sent to the user, and/or causing analytics associated with the eventto be sent to the user. In various embodiments, analytics may includeweb analytics, predictive analytics, retail analytics, marketinganalytics, customer analytics, business analytics, real-time analytics,digital analytics, and/or other similar statistical tools. For instance,a user may have indicated a user interest associated with the event of aparticular baseball player entering a game. The user may indicate thatwhen the particular player enters the game, they would like to receivean SMS message to be sent to their mobile device notifying them that theparticular player has entered the game. In various embodiments, the usermay opt-in or opt-out of action selection.

In various embodiments, and upon receiving, determining, generating,and/or extracting the frame, the frame may be analyzed to determine aregion of interest. In various embodiments, the frame is analyzed basedon the user interest. In various embodiments, the frame is analyzedbased on a system-generated user interest. In various embodiments, abounding box is determined and/or generated. A bounding box may also bereferred to as a “minimum bounding box” or a “minimum-perimeter boundingbox.” In some instances, the bounding box includes a rectangular areaconfigured to enclose a pixel and/or a plurality of pixels included inthe frame. In some instances, the bounding box may be configured toenclose an area that includes a pixel and/or plurality of pixels. Insome instances, the dimensions, length and width, may be predefined. Invarious embodiments, the bounding box may be defined by a polygon withn-sides where “n” represents a number of sides configured to enclose apixel and/or plurality of pixels. In various embodiments, the boundingbox may be defined by a three-dimensional shape. In some instances, thebounding box may be defined by a polyhedron and configured to enclose avolume. The bounding box may be used to detect regions of interest inthe frame. In some instances, image embedding, object detection,computer vision learning algorithms, object localization, and/or thelike are used to detect the regions of interest in the frame. Forinstance, a bounding box may be moved across the frame representing animage from a baseball game. The bounding box can determine regions ofinterest relating to objects in the frame (e.g., a bat, a glove, a hat,a shoe) and/or relating to persons in the frame (e.g., a human face). Insome instances, a plurality of frames can be individually analyzed todetermine a region of interest in each frame of the plurality of frames.

In various embodiments, the bounding box is moved across a frame todetermine if a region of interest is included in the frame. The boundingbox can be used to scan, analyze, determine, and/or detect the entireframe and/or a portion of the frame. In some instances, multiple regionsof interest can be detected in the frame. In some instances, the regionof interest may be associated with facial detection, object detection,emotion detection, and activity detection.

In various embodiments, the region of interest is compared to anidentified image and/or identified images representing various events ina database. In some instances, a similarity between the region ofinterest and a previously identified event may be determined. Forinstance, a similarity may be based on a cosine distance, a vectorcomparison, or a Euclidean distance between the region of interest andthe identified image. In some instances, a similarity between the regionof interest and the identified image may include comparing a similarityvalue with a predetermined similarity threshold. In some instances, theidentified images stored in the database may include identified objects,persons, faces, emotions, and/or activities.

In various embodiments, the database may include a knowledge base. Theknowledge base may include an ontology representing a plurality ofidentified images and relationships between identified images. Forinstance, the ontology may include a semantic graph that receives aninput value and outputs a node of the graph associated with the inputvalue. For instance, the ontology may include a hierarchy of MajorLeague Baseball including a graph with teams, games, and players. Invarious embodiments, the knowledge base may include embedded outputsfrom various neural network models. In some instances, the embeddedoutputs may be manually or automatically scraped from websites. Forinstance, the knowledge base may include machine representations of aface of a particular actress that is automatically scraped from awebsite and stored in the database. In various embodiments, multipleregions of interest may be aggregated into a single value and comparedto an identified image value. In some instances, a region of interestmay include a particular object and a particular face. A master valuecan be determined representing the particular object and the particularface and compared to an identified image and associated value. In someinstances, the master value can be compared to an identified image valuewith respect to a predetermined similarity threshold.

In various embodiments, a first region of interest from a first framecan be compared to a second region of interest in a second frame. Insome instances, the first region of interest and the second region ofinterest may be substantially located in a similar portion of the frame.

In various embodiments, human activity detection in multiple frames of avideo item may be performed. In various embodiments, human activitydetection may also be referred to as prose detection. In some instances,markers (e.g., points) may be placed on a human body within a frame of avideo item. For instance, points may be placed on the joints of a humanbody. In some instances, lines connecting the markers may be used togenerate a skeletal representation of a human body. In some instances,cross-frame analysis may be used to determine changes in the linesconnecting the markers in multiple frames. For instance, a marker on ahip joint, a knee joint, and an ankle joint on a human body may beconnected by lines to generate a representation of a human leg in afirst frame. The skeletal representation of the human leg may beanalyzed in subsequent frames to determine changes in leg position overtime. For instance, a frame may represent one second of a video. Therepresentation of the human leg may be analyzed in 20 sequential framesrepresenting 20 seconds of the video to determine changes in legposition. For instance, based on analyzing the 20 sequential frames itmay be determined that the leg is being used to walk, jog, or run. Invarious embodiments, human activity detection in multiple frames may becompared to identified human activities stored in the knowledge base. Invarious embodiments, human activities may be predicted based oncross-frame analysis over a period of time. In various embodiments, amachine learning model and/or neural network may be used to identify theregions of interest in a frame.

In various embodiments, the region of interest may be identified asbeing associated with user selection, a user interest, and/or an event.In some instances, the region of interest may be identified using amachine learning model and/or a neural network. For instance, a regionof interest in a frame may be determined to be a particular object or aparticular face using a machine learning model and/or neural network. Invarious embodiments, the identified region of interest is compared tothe user interest associated with an event (e.g., an object, a person,and/or an activity). In response to determining the region of interestis associated with the user interest, an action may be triggered.

The user may also indicate an action selection that is associated withthe event. For instance, the identified region of interest may indicatethat a particular baseball player has entered the game. The user mayhave previously indicated that she was interested in knowing when theparticular baseball player enters a game. Therefore, after theidentified region of interest may be determined to be associated withthe user interest an action is triggered. In some instances, theidentified region of interest may be determined to be different than theuser interests. For instance, if the identified region of interest andthe user interest are different, then the triggered action may bediscontinued, paused, stopped, and/or prevented from occurring.

In various embodiments, after triggering the action, the user mayprovide feedback data associated with the identified region of interestand/or the triggered action. The user may provide comments, corrections,or feedback indicating an accuracy and/or helpfulness of the identifiedregion and/or the triggered action.

For the purposes of this discussion, a video item, also referred hereinas video content, may be manifested in many different ways including,for example, as text-based items, audio items, video items, multimediaitems, graphical items, and so forth. Examples of the video item includetelevision shows, sporting events, concerts, movies, limited-series,documentaries, slide shows, graphical presentations, and graphicinterchange formats (GIFs). The video item may also be configured foruse in a virtual reality or augmented reality environment.

FIG. 1 illustrates an example system 100 for triggering an action basedon identifying an event in a video item. The system 100, may include aservice provider 102, a user 104, and an electronic device 106associated with the user 104. In various embodiments, the serviceprovider 102 may be any entity, server(s), platform, etc. that offersitems (e.g., products, services, etc.) to a user 104 via an electronicmarketplace (e.g., a website, a mobile application, etc.) associatedwith the service provider 102. That is, a user 104 may access theelectronic marketplace via a corresponding electronic device 106 for thepurpose of searching for, viewing, selecting, acquiring (e.g.,purchasing, leasing, renting, borrowing, lending, etc.) items, etc. Theitems may be provided directly by the service provider 102 or may beprovided by the service provider 102 on behalf of a different entity.Provided that the items are video items, the video items may beavailable through a variety of services including broadcast channels viaantenna and/or satellite, cable channels, cable and satellitereplacement services, subscription and non-subscription streamingservices, websites, mobile applications, pay-per-view services, and thelike. That is, via a website, an electronic marketplace, and/or a mobileapplication associated with the service provider 102, the users 104 mayplace orders for items. The electronic device 106 may be a mobile phone,a smart phone, a personal digital assistant, a portable media player, atablet computing device, a laptop computer, a desktop computer, adigital media player, a television, virtual and/or augmented realitydevices, gaming consoles, electronic book (eBook) reader devices, or thelike.

User data associated with the user 104 may be determined from the userprofile of a user 104. For instance, personal information (e.g.,address, telephone number, etc.), demographic information (e.g., age,gender, ethnicity, etc.), interests of the user 104 (e.g., sports,movies, hobbies, etc.), may be collected. Other user data may be storedon content server(s) 108 in association with the user profile of theuser 104. Such data may include the user's 104 activity with respect toa retail site associated with the service provider 102, such as searchhistory, purchase history, viewing history, a saved-items list (i.e., a“wish” list), reviews submitted by the user 104, and so on.

The service provider 102, may include, or be associated with, one ormore devices (e.g., content server(s) 108). Moreover, the contentservers 108 may contain any number of servers that are possibly arrangedas a server farm. Other server architectures may also be used toimplement the content server(s) 108. In various embodiments, the contentserver(s) may maintain one or more modules, such a user interest andsettings module 110, an action determination module 112, a framedetermination module 114, a frame analysis module 116, a region ofinterest analysis module 118, and/or a trigger action module 120.

In various embodiments, the user interest and settings module 110 ofcontent servers 108 may collect or determine user interest(s) 122 anduser action selection(s) 124. The user interest 122 is associated withan event (e.g., an object, person, and/or activity) that a user 104 isinterested in following. In some instances, the user 104 may indicatethey are interested in following whenever particular actress appears ina video item 126 and/or video item portion 128. The user interest may becollected by the user interest and settings module 110.

In some instances, the user 104 may indicate they are interested infollowing when their favorite baseball player comes up to bat. The user104 may indicate a plurality of events (e.g., objects, persons, andactivities) that they are interested in following. For example, the user104 may indicate that she is interested in the following: the user's 104two favorite baseball players, Robinson Cano and Nelson Cruz of theSeattle Mariners (persons); any player in Major League Baseball that iswearing a particular type of shoe (objects); and any Major LeagueBaseball player that hits a home run (activities). In variousembodiments, user interests 122 may be associated with user actionselections 124 and/or user preferences. In various embodiments, a user104 may indicate a user interest 122 via a website, mobile application,etc., that is accessible via an electronic device 106. In someinstances, the user 104 may access a mobile application associated withan internet video and/or audio on demand service and select and/or inputsettings related to a user interest 122 (e.g., preferences, alerts,notifications, and/or alarms). In various embodiments the user 104 maysend a short message service message (SMS) and/or multimedia messagingservice (MMS) message indicating a user interest 122 to the userinterest and settings module 110. In various embodiments, the user 104may indicate audibly a user interest 122 to a voice-controlled device, asmart speaker, and/or similar technology. In particular, the user 104may audibly utter a voice command that is captured by one or moremicrophones of a device. The device and/or a cloud-based serviceassociated therewith may process the voice command (e.g., an audiosignal representative of the voice command) using automated speechrecognition (ASR) techniques, natural language understanding (NLU)techniques, and so on. In some embodiments, the device may output (e.g.,audibly via one or more speakers, visually via a display, etc.) anotification to the user that is responsive to the voice command andthat is related to an event. In some instances, a user 104 may selectoptions from predefined fields or provide comments. For instance, a user104 may select, “Robinson Cano” from a predefined field containingplayers that may be followed. A user 104 may also provide in plain-textin a text entry field, “I would like to follow Robinson Cano.”

In various embodiments, the user interest 122 may be system generatedand collected by the user interest and settings module 110. In someinstances, the user interest 122 may be based on historical informationassociated the user including previous purchases or previously consumedvideo items. In some instances, the user interest 122 may be based onuser settings and or user preferences. For example, a user interest 122may be based on a user's 104 age, location, type of employment, and/orgender. In addition, a user interest 122 may be based on a user's 104social media activity. In some instances, a user interest 122 may beassociated with a user 104 providing inputs to a selectable field,predefined, or comment field indicating an event (e.g., object, person,or activity) that they are interested in. In various embodiments, a user104 may be included in a user group by the service provider 102. Theuser 104 may be grouped with other users with similar interests based oncollected and determined user interests. The user group may receiverecommendations indicating a potential user interest. For instance, auser 104 may be included in a “Seattle Sports” user group based on theiruser settings. The “Seattle Sports” user group may be automaticallyprovided with a recommendation to follow particular players on sportsteams located in and around Seattle, Wash. In various embodiments, theuser interest 122 identifies events (e.g., objects, persons, oractivities) that a user 104 is interested in.

In various embodiments, the user interest and settings module 110,collects user action selections 124. The user action selection 124 maybe associated with an opt-in to connect a user interest 122 to atriggerable action. A user 104 may also opt-out of a triggerable action.In addition, a user action selection 124 may be associated withselecting a type of action that the user 104 would like to connect to auser interest 122. In some instances, a default action may be associatedwith a user interest 122. For instance, a user 104 on their tabletcomputing device may select actions from a drop-down list in a usersettings and/or preferences portion of their account settings via awebsite. In various embodiments, the user 104 may be presented withpotential user selections based on a user interest 122. For example, auser 104 may be interested in following a musician performing livein-concert. The user 104 indicates that the musician is a user interest122. The user 104 may then be presented with potential user selectionsthat are available to connect with the musician performing live. Forinstance, only a subset of actions 130 may be available for selection.

In various embodiments, the action determination module 112 of thecontent servers 108 may receive user action selections 124 from the userinterest and settings module. Based on the user action selection 124, anaction 130 may be determined. For instance, the user action selection124 may indicate the user 104 has opted-in to actions based on a userinterest 122. In addition, the user action selection 124 may indicateactions the user 104 would prefer based on a user interest 122. Forinstance, user action selections 124 may indicate a user would like toreceive SMS and MMS updates, but not e-mail messages, based on a userinterest 122. The actions 130 may include receiving a notification,receiving an SMS, receiving an MMS, receiving an e-mail message, causingthe video item to be presented via an electronic device, causing anelectronic device to be powered on and/or off, causing an electronicdevice to increase and/or decrease in audio volume, recording a videoitem or video item portion of a video item (e.g., record some, but notall, of the video item), causing an electronic device to identify asecond electronic device, causing an electronic device to becommunicatively coupled to a second electronic device, causing a summaryof the event to be sent to the user 104, and/or causing analyticsassociated with the event to be sent to the user 104. For instance, auser 104 may have indicated a user interest 122 associated with theevent of a particular baseball player entering a game. The user 104 mayindicate that when the particular player enters the game, they wouldlike to receive an SMS message to be sent to their mobile devicenotifying them that the particular player has entered the game. Thatway, the user 104 may begin watching the game while that particularplayer is on the field, is batting, etc. In various embodiments, theaction determination module 112 determines an action selection 132 basedon the user action selection 124 and the action(s) 130.

In various embodiments, the frame determination module 114 may receive avideo item 126 or a video item portion 128. The frame determinationmodule 114 then determines a frame or a plurality of frames associatedwith the video item 126. A video item 126 may be broken into individualframes 134. A frame 134 may include an image, or a still image,representing a time period, interval, and/or instance of the video item126. For instance, a frame 134 may be a still image representing1/100^(th) of the video. In some instances, the video item 126 can bedivided into a predetermined number of frames 134. For instance, thevideo item 126 may be divided based on a predetermined ratio (e.g., oneframe per 1/100^(th) of a video) and/or based on a predetermined timeperiod. For instance, a frame 134 may represent one second of a videoitem 126. In some instances, the number of frames 134 may be a singleframe representation of the video 126. In some instances, a maximumnumber of frames 134 may be based on the frame rate of the video item126. For instance, a 60-second video item 126 with a frame rate of60-frames-per-second may be broken into 60 frames 134.

In some instances, a video item 126 may be divided into frames 134 thateach represent a fixed interval. For example, a 60-second video item 126may be broken into 15 frames 134. Each frame represents four seconds ofthe video item 126. In some instances, a video item 126 may be dividedinto frames 134 that each represent a varying interval. For example, a60-second video item 126 may be broken into 15 frames 134, wherein tenof the 15 frames 134 each represent five seconds of the video item (50seconds) and the remaining five of the 15 frames 134 each represent twoseconds of the video item (10 seconds). In other words, a first portionof the video item 126 may be broken up into frames 134 based on a firsttime interval per frame 134 and a second portion of the video item 126may be broken up into frames 134 based on a second time interval perframe 134. In various embodiments, the frame determination module 114may also receive a frame 134 via the service provider 102 or a differentthird party resource.

In various embodiments, the frame analysis module 116 receives the userinterest 122 and the frame 134 and analyzes the frame to determine aregion of interest 136. In various embodiments, the frame 134 isanalyzed based on the user interest 122. For instance, the user interest122 may indicate a user 104 is interested in an object (e.g., abaseball). The frame 134 is then analyzed to identify a region ofinterest 136 that contains a baseball. The frame 134 can also beanalyzed based on a system-generated user interest 122.

In various embodiments, a bounding box 138 may be used to analyze theframe 134. The bounding box 138 includes a two-dimensional (2-D)geometric shape configured to enclose a pixel or a plurality of pixelsincluded in the frame 134. In some instances, image embedding, computervision algorithms, object localization, and or/the like are used todetect a region of interest 136 in a frame 134. In some instances,facial detection, object detection, and/or emotion detection are used toanalyze the frame 134. For instance, the bounding box 138 may be arectangular area that encloses a plurality of pixels in the frame 134.In some instances, the dimensions of the bounding box may be predefined.For instance, the shape, the length, the width, and similar 2-Ddimensions of geographic shapes may be predefined. In the example of arectangular area, a length and a width of the bounding box 138 may bepredefined. The bounding box 138 may be used to detect a region ofinterest 136 within the frame. For instance, the bounding box 138 can bemoved across the frame 134. In some instances, a plurality of frames canbe analyzed to determine a region of interest in each frame 134 of theplurality of frames. In some instances, one frame 134 may containmultiple regions of interest 136. In various embodiments, the boundingbox 138 can be used to scan, analyze, determine, and/or detect a regionof interest 136 in the entire frame 134 or a portion of the frame 134.For instance, a user 104 may indicate a user interest 122 associatedwith interest in the guitars (i.e., objects) in the movie “A Hard Day'sNight” starring the Beatles. The movie may be broken down into apredetermined number of frames 134. Each frame 134 may then be analyzedusing at least the bounding box 138. The bounding box 138, may thenidentify a region of interest 136 in one of the frames 134 that maycontain a guitar. The region of interest 136 may then be sent to theregion of interest analysis module 118 for analysis.

In various embodiments, and after analyzing the frame 134 using at leastthe techniques described herein, a region of interest 136 is provided tothe region of interest analysis module 118. The region of interestanalysis module 118 compares a region of interest 136 with identifiedimages in a database 140. The database 140 may include a knowledge base142. The knowledge base 142 may include an ontology representing aplurality of identified images associated with objects, faces, persons,emotions, and/activities and relationships between the identifiedimages. That is, the database 140 may include images that depict knownobjects, faces, persons, activities, and so on. The ontology may includea semantic graph, and/or similar graphs, that are configured to receivean input (e.g., a region of interest 136) and output a node of the graphassociated with the input. For instance, the ontology may include ahierarchy of guitar brands, designs, shapes, types, and/or colors. Invarious embodiments, the knowledge base may include a semanticunderstanding of an event. For instance, the knowledge base may includea semantic understanding of the rules of a sport (e.g., baseball). Theknowledge base 142 may include machine representations of objects,faces, persons, emotions, and/or activities. The knowledge base 142 maybe pre-populated. In some instances, the database 140 and/or theknowledge base 142 may automatically scrape public-websites for imagesand image annotations. For instance, the knowledge base 142 may scrapeimages and image annotations associated with guitars from apublic-website to store in the database 140.

In various embodiments, a region of interest 136 is compared to anidentified image in the database 140. A similarity between the region ofinterest 136 and images in the database 140 may be determined. Forinstance, a similarity value may be based on a cosine distance, a vectorcomparison, a Euclidean distance, or similar techniques to determine asimilarity between the region of interest 136 the identified image inthe database 140. In some instances, the similarity between the regionof interest 136 and the identified image may include comparing asimilarity value with a predetermined similarity threshold (alsoreferred to herein as “predetermined similarity threshold value”). Theregion of interest 136 and the identified image may be determined to beassociated with one another if a similarity value is meets or exceedsthe predetermined similarity threshold. Conversely, the region ofinterest 136 and the identified image may be determined to not beassociated with one another if a similarity value is below thepredetermined similarity threshold.

In various embodiments, the region of interest analysis module 118 mayreceive multiple regions of interest 136 for analysis. For instance, theregion of interest analysis module 118 may receive a first region ofinterest from a first frame of a video item 126 and a second region ofinterest from a second frame of the video item 126. The region ofinterest analysis module 118 may perform cross-frame analysis 143 todetermine changes between the first frame and the second frame. Forinstance, the first region of interest in the first frame may enclose a10×10 grouping of pixels in the top, right corner of the first frame.The second region of interest in the second frame may similarly enclosea 10×10 grouping of pixels in the top, right corner of the second frame.Therefore, the location first region of interest in the first framesubstantially corresponds to a location of a second region of interestin the second frame. In various embodiments, the first region ofinterest and the second region of interest may be compared to determinea change in the 10×10 grouping of pixels. In various embodiments,cross-frame analysis 143 may be used to determine correlations betweenframes. For instance, cross-frame analysis 143 may be used to correlatehuman body positions in sequential and/or non-sequential frames or tolocalize an activity in multiple frames. In various embodiments, thefirst region of interest and the second region of interest may fully,substantially, partially, or not be located in a portion of the frame134.

In various embodiments, the region of interest analysis module 118 mayuse a machine learning model and/or neural network to analyze a regionof interest 136 and to identify a region of interest 136 in a frame 134.In some instances, the machine learning model may be introduced manualannotations of identified objects. For instance, the machine learningmodel may receive a picture of a ball that is annotated, “this is aball.” In some instances, the machine learning model may be introduced aplurality of manual annotations of identified objects. The machinelearning model may then be introduced an unidentified object. Themachine learning model may then produce or generate an output valuerepresenting an identification of the unidentified object. The outputvalue may then be provided to a neural network.

In various embodiments, a neural network may be referred to as anartificial neural network or an artificial intelligence neural network.In some instances, the neural network may be a processing device, analgorithm, and/or hardware. In some instances, the neural networkincludes at least a processing component, a network, and a structure.The neural network identifies the object by producing a neural networkoutput. The neural network output may include a numerical valueassociated with a vector output and/or a vector of features, a classvalue indicating a semantic answer (e.g., “this is a ball”), and/orplain-text. The output(s) of the neural network are then compared tomanual annotations to determine if the output of the neural network isaccurate. In some instances, the neural network output is accurate if itis within a predetermined error range when compared to the manualannotations. If an error between neural network output and the manualannotation is acceptable (e.g., within the predetermined error range)the machine learning model is determined to be trained to identify anevent (e.g., an object, a face, an activity). If the error between theneural network output and the manual annotation is not consideredacceptable (e.g., outside of the predetermined error range) the machinelearning model is not determined to be trained. In various embodiments,a neural network is used for each type of event. For instance, a firstneural network to identify objects, a second neural network to identifyfaces, and a third neural network to identify activities. In variousembodiments, a single neural network is used to identify objects, faces,and activities.

In various embodiments, the region of interest analysis module 118outputs a trigger event 144. The trigger event 144 is associated withthe region of interest analysis module 118 identifying the region ofinterest 136. For instance, using the example from above, the triggerevent 144 maybe be that a guitar was identified in the movie a “A HardDay's Night” corresponding to the user interest 122. The trigger event144 and the action selection 132 are then sent to the trigger actionmodule 120.

In various embodiments, the trigger action module 120 receives theaction selection 132 and the trigger event 144 and causes an occurrenceof an action 130 to be performed. For instance, the trigger actionmodule 120 may cause an action 130 including at least one of causing anotification to be sent to an electronic device 106, causing an SMS oran MMS to be sent to an electronic device 106, causing an electronicdevice 106 to be powered on and/or off, causing an electronic device 106to increase and/or decrease in audio volume, recording a video item 126or video item portion 128, causing the electronic device 106 to identifya second electronic device, causing the electronic device 106 to becommunicatively coupled to a second electronic device, causing a summaryof the event to be sent to an electronic device 106, or causinganalytics associated with the trigger event 144 to be sent to theelectronic device 106.

FIG. 2 illustrates an example system that includes multiple devices thatfacilitate the triggering of an action based on identifying an event ina video item. More particularly, the system 200 may include the serviceprovider 102, a user 104, an electronic device 106 associated with theuser 104, and one or more network(s) 202. As shown, the service provider102 may include, or be associated with, the one or more contentserver(s) 108, which may include one or more processor(s) 204 andcomputer-readable media 206. The computer-readable media 206 maymaintain or store the user interest and settings module 110, the actiondetermination module 112, the frame determination module 114, the frameanalysis module 116, the region of interest analysis module 118, thetrigger action module 120, and possibly other modules that perform theoperations described herein.

For the purposes of this discussion, the service provider 102 may be anyentity, server(s), platform, service, etc. that facilitates operationsto be performed by the user interest and settings module 110, the actiondetermination module 112, the frame determination module 114, the frameanalysis module 116, the region of interest analysis module 118, and/orthe trigger action module 120. In particular, the service provider 102may maintain a platform or site in which a user 104 may access via anelectronic device 106 in order to indicate a user interest 122, indicatea user action selection 124, and/or to manage user settings and/or userpreferences. The video item 126 may be provided to the service provider102 by one or more entities that author, create, produce, broadcast, orfacilitate presentation of the video item 126. The service provider 102may also be associated with a retail marketplace (e.g., a website) thatallows a user 104 to search for, browse, view, borrow, return, acquire,etc. items (e.g., products, services, digital items, etc.) that aremaintained by the service provider 102, and that are possibly maintainedon behalf of other entities (e.g., artists, authors, publishers,vendors, other service providers, merchants, etc.). In certainembodiments, the service provider 102 may offer a video service in whichvideo items (e.g., stored video items, live video items, etc.) may beconsumed by users 104.

FIG. 3 is a pictorial diagram 300 of an illustrative process todetermine region(s) of interest with respect to a frame of a video item.The pictorial diagram 300 includes an example of identifying regions ofinterest 136(1)-136(6) within a frame 134(1). Depicted within frame134(1) are two persons participating in a game of a baseball. Frame134(1) may be a snapshot and/or screenshot representing a time period ofa live-stream or a pre-recorded baseball game. A first player 302 iscrouched preparing to receive a pitch (e.g., a baseball being thrown). Asecond player 304 is “at-bat” and preparing to attempt to hit thebaseball being thrown to the first player 302. A bounding box 138 isused to analyze the frame 134(1).

The bounding box 138 may be moved across the pixels contained withinframe 134(1) to scan, analyze, determine, and or detect regions ofinterest 136(1)-136(6). The bounding box 138 may include a rectangulararea to enclose and analyze a set of pixels contained within the frame134(1). In various embodiments, the bounding box 138 may be anothergeometric shape. The bounding box 138 within frame 134(1) maybe be basedon predetermined dimensions. The bounding box 138 may be used to detectobjects, persons, and/or faces as shown in pictorial diagram 300. Invarious embodiments, the bounding box 138 may be used to detectactivities. The bounding box 138 may be used to analyze frame 134(1) toidentify a region of interest 136 that is related to a user interest122. The bounding box 138 is showing two regions of interest 136(1) and136(6) associated with the first player 302 and four regions of interest136(2)-136(5) associated with the second player. As shown, the firstregion of interest 136(1) appears to depict a glove being worn by thefirst player 302, the second region of interest 136(2) appears to depicta head of the second player 304, the third region of interest 136(3)appears to depict a baseball that the second player 304 is attempting tohit, the fourth region of interest 136(4) appears to depict a bat beingheld and swung by the second player 304, the fifth region of interest136(5) appears to depict a right foot of the second player 304, and thesixth region of interest 136(6) appears to depict a left foot of thefirst player 302.

FIG. 4 is a diagram 400 of an illustrative process to analyze regions ofinterest 136(1)-136(6). Regions of interest 136(1)-136(5) are comparedto identified images 402 in a database. Region of interest 136(6) is notassociated with an identified image 402(6) in the database 140. Invarious embodiments, the region of interest analysis module 118 maycompare regions of interest 136(1)-136(6) with images 402 that stored ina knowledge base of the database 140. The knowledge base may include anontology representing a plurality of identified images associated withobjects, faces, persons, emotions, and/or activities and theirrelationships in the database 140.

Regions of interest 136(1)-136(5) may be identified as depicting variousobjects and a human face based on a comparison with images 402 in thedatabase. Region of interest 136(6) may have insufficient graphicalinformation to be identified by the database 140. In some instances, thedatabase 140 may not contain an image 402(6) that corresponds to theregion of interest 136(6). In various embodiments, the database 140and/or a knowledge base may be pre-populated with images. In someinstances, the database 140 and/or the knowledge base may scrapepublic-websites from images 402 and image annotations and store theimages 402 in the database 140. Regions of interest 136(1)-136(5) may beassociated with images 402(1)-402(5) based on a similarity value. Forinstance, region of interest 136(1) may be determined to be associatedwith image 402(1) based on a similarity value. The similarity value maybe based on a cosine distance, a vector comparison, a Euclideandistance, and/or similar metrics quantifying the similarity between theregion of interest 136(1) and image 402(1). The region of interest136(1) and image 402(1) may have a similarity value that is equal to orexceeds a predetermined similarity threshold. In various embodiments,the region of interest 136(1) and image 402(1) may be compared based ona pixel-by-pixel comparison. In various embodiments, an object, face,etc., depicted in a region of interest 136 may be determined to besimilar to a known object, face, etc., depicted in stored image 402based on a meeting a condition (e.g., determined similarity valuemeeting or exceeding a similarity threshold amount and/or being within asimilarity threshold range). If the condition is met, it may bedetermined that the region of interest 136 matches an image 402 storedin the database. Conversely, region of interest 136(6) and image 402(6)may have a similarity value that is less than a predetermined similaritythreshold. Therefore, region of interest 136(6) may not be identified.

As shown, the first region of interest 136(1) appears to be identifiedas a glove 402(1), the second region of interest 136(2) appears to beidentified as a head 402(2), the third region of interest 136(3) appearsto be identified as a baseball 402(3), the fourth region of interest136(4) appears to be identified as a bat 402(4), and the fifth region ofinterest 136(5) appears to be identified as a right foot 402(5). Theobjects and head depicted in regions of interest 136(1)-136(5) may becompared respectively to images 402(1)-402(5). Based on the comparison,a similarity value may have been determined representing an amount ofsimilarity between two images. In some instances, the similarity valuemay have been equal to or exceeded or a predetermined similaritythreshold. In some instances, the similarity value may have been withina predetermined similarity range. Therefore, the similarity valuesbetween regions of interest 136(1)-136(5) and images 402(1)-402(5) maybe a threshold amount of similarity. As shown, the sixth region ofinterest 136(6) appears to be unidentified as shown by the question mark402(6). The object in region of interest 136(6) remains unidentifiedwith a previously stored image 402 in database 402. In some instances,the objection in region of interest 136(6) was compared to a pluralityof images 402 stored in the database 140. Based on the comparison(s), asimilarity value may have been determined representing an amount ofsimilarity between region of interest 136(6) and an image 402 stored inthe database. In some instances, the similarity value may have beenlower than predetermined similarity threshold. In some instances, thesimilarity value may have been outside of a predetermined similarityrange. Therefore, the similarity value did not satisfy a thresholdamount of similarity.

FIG. 5 is a pictorial diagram 500 an illustrative process to analyzeregions of interest 136(7) and 136(8) included in frames 134. The regionof interest analysis module 118 may perform cross-frame analysis 143 todetermine a change between frame 134(2) and 134(3). For instance, frame134(2) may be a snapshot and/or screenshot representing a first timeperiod of a live-stream or a pre-recorded baseball game. Frame 134(3)may be a snapshot and/or screenshot representing a second time period ofthe live-stream or a pre-recorded baseball game. Frame 134(3) may betemporally after frame 134(2). In various embodiments, more than twoframes 134 may be used to perform cross-frame analysis 143. In variousembodiments, at least two frames 134 may be arranged chronologicallyand/or sequentially and analyzed.

Frame 134(2) includes bounding box 138(1) enclosing a first portion ofpixels. Frame 134(3) includes bounding box 138(2) enclosing a secondportion of pixels. Bounding box 138(1) and bounding box 138(2) may besubstantially the same shape and/or dimensions. In various embodiments,they may be different shapes, sizes, and/or dimensions. In variousembodiments, bounding box 138(1) may be located in a portion of frame134(2) that substantially corresponds or partially corresponds to alocation of bounding box 138(2) in frame 134(3). In various embodimentsbounding box 138(1) may be configured to enclose an entirety of frame134(2) and bounding box 138(2) may be configured to enclose an entiretyof frame 134(3).

Region of interest 136(7) includes a first snap shot 502 representingobjects, a player, and activities in a baseball game during a first timeperiod. Region of interest 136(8) includes a second snap shot 504representing the objects, the player 506, and activities in the baseballgame during a second time period. Regions of interest 136(7) and 136(8)may be compared with respect to the body position of the player 506, theface of the player 506, and/or the location of the objects includingwithin the respective regions of interest. In various embodiments, theobjects and/or faces including in regions of interest 136(7) and 136(8)can be identified using the region of interest analysis module 118. Invarious embodiments, markers (e.g., points) may be placed on a humanbody (e.g., on player 506) included in frame 134(2). For instance,points may be placed on the joints of a human body. In some instances,lines connecting the markers may be used to generate a skeletalrepresentation the player 506. For instance, a marker on a wrist joint,elbow joint, and/or a shoulder joint may be connected by lines togenerate a representation of an arm of the player 506. Therepresentation of the arm of player 506 may be analyzed in frames 134(2)and 134(3) to determine changes in arm position over time. In variousembodiments, human activity detection in multiple frames may be comparedto identified human activities stored in a database. For instance,region of interest 136(7) includes the player 506 swinging at a ballduring a first time period. Region of interest 136(8) includes theplayer 506 completing a swing during a second time period and depicts abaseball being hit. The human activity of “hitting a ball” may beidentified.

FIG. 6 is a flow diagram illustrating an example process 600 ofdetermining inputs and outputs of a machine learning model. Moreover,the following actions described with respect to FIG. 6 may be performedby the service provider 102 and/or the content server(s) 108, asillustrated in, and described with respect to FIGS. 1 and 2.

Block 602 illustrates determining inputs 604 for a machine learningmodel. The inputs 602 may be manual annotations of identified objects.For instance, the machine learning model may receive a picture of a hatthat is annotated “hat.” In various embodiments, the inputs 602 mayinclude a frame 134 of a video item 126. In various embodiments feedbackdata 606 may be used as an input 602. In some instances, a previousoccurrence of an action was triggered. In response, a user 104 mayprovide feedback data 606 in the form of comments, corrections, and/orfeedback indicating an accuracy and/or helpfulness of the identifiedregion and/or the triggered action. For instance, a user 104 may providefeedback data 606 based on selections in predefined fields (e.g., thumbsup/down, a scale from 1-10, a rating of 1-5 stars, or the like). Invarious embodiments, a user 104 may provide feedback data 606 in theform of comments in a provided field.

Block 608 illustrates training the model using the inputs 604. Themachine learning model may be introduced an unidentified object andgenerate an output 610 in response. In some instances, the output 610,is a quantitative value. The output 610 may be provided to a neuralnetwork as a neural network input. In various embodiments, the neuralnetwork can be used to determine if the machine model is trained. Theneural network uses the output 610 to identify the unidentified objectby producing a neural network output. The neural network output mayinclude a numeric value associated with a vector output and/or a vectorof features, a class value indicating a semantic answer (e.g., “this isa ball”), and/or plain-text. The output(s) of the neural network arethen compared to manual annotations to determine if the output of theneural network is accurate. In some instances, the neural network outputis accurate if it is within a predetermined error range when compared tothe manual annotations. If an error between neural network output andthe manual annotation is acceptable (e.g., within the predeterminederror range), the machine learning model is determined to be trained toidentify an event (e.g., an object, a face, an activity).

Block 612 illustrates determining that a region of interest isassociated with a user selection using the trained machine learningmodel. In various embodiments, the trained machine learning model may beused to identify an object included with a region of interest. In someinstances, the object is identified by the trained machine learningmodel. In various embodiments, the identified object may be compared toa user interest 122 to produce a similarity value. In some instances,the similarity value may be compared with a predetermined similaritythreshold. If the similarity value equals to or exceeds thepredetermined similarity threshold, the region of interest is associatedwith the user interest 122.

FIG. 7 is a flow diagram illustrating an example process 700 ofdetermining an action based on identifying an event in a video item.Moreover, the following actions described with respect to FIG. 7 may beperformed by the service provider 102 and/or the content server(s) 108,as illustrated in, and described with respect to FIGS. 1 and 2.

Block 702 illustrates receiving user information 704 associated with auser 104. User information 704 may include a user location 704(1), auser purchasing history 704(2), a user age 704(3), user interests704(4), and/or user settings 704(5). For instance, user settings 704(5)may indicate that a user 104 has opted-in to receiving notificationswith respect to user interests 122.

Block 706 illustrates determining, based at least in part on the userinformation 704, a pre-configured action 708. In various embodiments,the user information 704 may be used to recommend a pre-configuredaction 708 that a user 104 may select. For instance, a user purchasinghistory 704(2) may indicate a user 104 has recently purchased anintelligent personal assistant and/or smart speakers. Therefore, a user104 may be recommended a pre-configured action 708 such as audionotifications via the intelligent personal assistant and/or smartspeakers. In various embodiments, a user 104 may opt-in to therecommended, pre-configured action 708.

Block 710 illustrates causing, based at least in part on determiningthat a region of interest is associated with a user selection, anoccurrence of the pre-configured action 708. In various embodiments, thepre-configured action 708 is triggered. For instance, a user 104 mayhave been recommended, and subsequently opted into by user 104, audionotifications via an intelligent personal assistant and/or smartspeakers. In various embodiments one or more pre-configured actions 708may be triggered in response to determining that a region of interest isassociated with a user selection. Pre-configured actions 708 may includereceiving a notification, receiving an short message service (SMS)message, receiving a multimedia messaging service (MMS) message,receiving an e-mail message, causing the video item to be presented viaan electronic device, causing an electronic device to be powered onand/or off, causing an electronic device to increase and/or decrease inaudio volume, recording a video item and/or video item portion, causingan electronic device to identify a second electronic device, causing anelectronic device to be communicatively coupled to a second electronicdevice, causing a summary of the event to be sent to the user 104,and/or causing analytics associated with the event to be sent to anelectronic device associated with the user 104.

FIG. 8 is a flow diagram illustrating an example process 800 oftriggering an action based on identifying an event in a video item.Moreover, the following actions described with respect to FIG. 8 may beperformed by the service provider 102 and/or the content server(s) 108,as illustrated in, and described with respect to FIGS. 1 and 2.

Block 802 illustrates receiving first data associated with a userselection of a user. In various embodiments, a user 104 provides a userselection indicating an event (e.g., an object, a person, a face, and/oran activity) associated with the event. For instance, a user 104 mayindicate that they are interested in a particular character in atelevision show. In various embodiments, the user selection may beassociated with user settings and/or user preferences. In someinstances, the user selection may be associated with interests of theuser. In various embodiments, the user selection may be in response to asystem generated recommendation.

Block 804 illustrates receiving second data associated with apre-configured action. In various embodiments, a user 104 may indicatean action to associated with the events. In some instances, thepre-configured action may include receiving a notification, receiving anSMS, receiving an MMS, receiving an e-mail message, causing the videoitem to be presented via an electronic device, causing an electronicdevice to be powered on and/or off, causing an electronic device toincrease and/or decrease in audio volume, recording a video item orvideo item portion, causing a summary of the event to be sent to theuser, and/or causing analytics associated with the event to be sent tothe user. In various embodiments, multiple pre-configured actions may beindicated by the user 104.

Block 806 illustrates determining a plurality of frames associated witha live streaming video. In various embodiments, a frame may include animage representing a time period, interval, and/or instance of the livestreaming video. The live streaming video can be broken into apredetermined number of frames. In some instances, the live streamingvideo may be previously broken into frames and received by the contentservers.

Block 808 illustrates determining a bounding box associated with a frameof the plurality of frames. In various embodiments, the bounding box isassociated with analyzing a particular frame. In various embodiments, abounding box includes a rectangular area and is configured to enclose aset of pixels included in the frame. In some instances, the bounding boxmay be a predefined size, shape, and/or dimensions. In some instances,the bounding box may be configured to enclose an entirety of a frame.

Block 810 illustrates analyzing a frame using at least the bounding box.In various embodiments, the bounding box may be used to detect regionsof interest in the frame. For instance, a bounding box may be movedacross the pixels included in the frame to determine regions ofinterest. In various embodiments, the bounding box may use objectdetection, facial detection, activity detection, emotion detection,computer vision learning algorithms, object locations, and/or the liketo detect regions of interest in the frame.

Block 812 illustrates determining a region of interest within a frame.In various embodiments, a bounding box may be used to determine theregion of interest within the frame. In various embodiments, thebounding box may identify an object, a face, and/or an activity includedin the frame.

Block 814 illustrates comparing a region of interest and a plurality ofimages previously stored in a database. In various embodiments, theregion of interest and images previously stored in a database and/orknowledge base may be compared to determine a similarity value. Forinstance, a similarity value may be based on a cosine distance, a vectorcomparison, or a Euclidean distance between the region of interest andan image stored in the database. In various embodiments, the databaseand/or knowledge base may store images and associated image annotations.

Block 816 illustrates determining that a similarity value representing asimilarity between a region of interest an image is equal to or exceedsa predetermined similarity threshold.

Block 818 illustrates determining that the region of interest isassociated with the user selection. In various embodiments, a machinelearning model and/or neural networks may be used to determine that theregion of interest is associated with the user selection. For instance,a user selection may indicate a user 104 is interested in a particularactor wearing a particular brand of running shoes (e.g., a userselection). In addition, the region of interest may include theparticular actor is wearing running shoes. The machine learning modelmay identify the particular is wearing a brand of running shows thatcorresponds to the user selection. Therefore, the region of interest maybe associated with the user selection.

Block 820 illustrates causing an occurrence of a pre-configured action.In various embodiments, the pre-configured action may be selected by auser 104. In various embodiments, multiple pre-configured actions may betriggered based on a region of interest being associated with a userselection and/or user interests.

Note that the various techniques described above are assumed in thegiven examples to be implemented in the general context ofcomputer-executable instructions or software, such as program modules,that are stored in computer-readable storage and executed by theprocessor(s) of one or more computers or other devices such as thoseillustrated in the figures. Generally, program modules include routines,programs, objects, components, data structures, etc., and defineoperating logic for performing particular tasks or implement particularabstract data types.

Other architectures may be used to implement the describedfunctionality, and are intended to be within the scope of thisdisclosure. Furthermore, although specific distributions ofresponsibilities are defined above for purposes of discussion, thevarious functions and responsibilities might be distributed and dividedin different ways, depending on particular circumstances.

Similarly, software may be stored and distributed in various ways andusing different means, and the particular software storage and executionconfigurations described above may be varied in many different ways.Thus, software implementing the techniques described above may bedistributed on various types of computer-readable media, not limited tothe forms of memory that are specifically described.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as exemplary forms ofimplementing the claims.

What is claimed is:
 1. A method comprising: receiving a first indicationassociated with an event; receiving a second indication associated witha pre-configured action associated with the event; determining a frameof a video, wherein the frame represents an image during a time periodof the video and the video being a live video; determining a region ofinterest that includes a set of pixels included in the frame; comparingthe region of interest and a plurality of events that include the event;determining, based at least in part on comparing the region of interestand the plurality of events, that the region of interest indicates thatthe event has yet to occur in the video but is likely to occur in thevideo within a predetermined period of time; and causing, based at leastin part on determining the region of interest indicates that the eventhas yet to occur in the video but is likely to occur in the video withinthe predetermined period of time, and prior to a first occurrence of theevent in the video, a second occurrence of the pre-configured action. 2.The method as recited in claim 1, further comprising: determining inputsfor a machine learning model, the inputs including the frame; andtraining the machine learning model using the inputs to determine atleast one of an object, a human face, or an activity, and whereindetermining that the region of interest is associated with the eventfurther comprises determining that the region of interest is associatedwith the event using the machine learning model.
 3. The method asrecited in claim 1, further comprising: determining a second frame ofthe video, wherein the second frame represents a second image during asecond time period of the video; determining a second region of interestthat includes a second set of pixels included in the second frame; anddetermining, based at least in part on comparing the region of interestand the second region of interest, a frame change between the frame andthe second frame, the frame change representing a difference between theset of pixels in the frame and the second set of pixels in the secondframe, and wherein determining the region of interest is associated withthe event is further based at least in part on the frame change.
 4. Themethod as recited in claim 1, further comprising: receiving userinformation, the user information including at least one of a userinterest, a user age, a user location, or a user purchase; determining,based at least in part on the user information, an additionalpre-configured action; and causing, based at least in part ondetermining the region of interest is associated with the event, a thirdoccurrence of the additional pre-configured action.
 5. The method asrecited in claim 1, wherein the event includes at least one of anobject, a face, or an activity, and further comprising: determining,based at least in part on comparing the region of interest and theplurality of events, that a similarity value representing a similaritybetween the region of interest and the event equals to or exceeds apredetermined similarity threshold, and wherein determining the regionof interest is associated with the event is further based at least inpart on the similarity.
 6. The method as recited in claim 1, wherein thefirst indication includes at least one of a user input, a userselection, or a user setting, and wherein the pre-configured actionincludes at least one of: causing a notification to be sent; causing ashort message service (SMS) message to be sent; causing a multimediamessaging service (MMS) message to be sent; causing an email message tobe sent; causing at least a portion of the video to be stored; causingan electronic device to be powered on; causing the electronic device tobe powered off; causing an increase in audio volume associated with theelectronic device; causing a decrease in the audio volume associatedwith the electronic device; causing the electronic device to present thevideo; causing the electronic device to identify a second electronicdevice; causing the electronic device to be communicatively coupled tothe second electronic device; causing a summary of at least a portion ofthe video to be sent; or causing analytics associated with at least aportion of the video to be sent.
 7. The method as recited in claim 1,wherein analyzing the frame further comprises: determining a boundingbox, the bounding box including an area and configured to enclose aplurality of pixels included in the frame; and analyzing at least afirst portion of the frame using the bounding box.
 8. The method asrecited in claim 1, further comprising: determining an additional regionof interest, the additional region of interest including an additionalset of pixels included in the frame; comparing the additional region ofinterest and the plurality of events; and determining, based at least inpart on the comparing the region of interest and the plurality ofevents, that the additional region of interest is not associated withthe event.
 9. A system comprising: one or more processors; and memorystoring computer-executable instructions that, when executed, cause theone or more processors to perform acts comprising: receiving a firstindication associated with an event; receiving a second indicationassociated with a pre-configured action associated with the event;determining a frame of a video, wherein the frame represents an imageduring a time period of the video and the video being a live videooccurring in real-time; determining a region of interest that includes aset of pixels included in the frame; comparing the region of interestand a plurality of events that include the event; determining, based atleast in part on comparing the region of interest and the plurality ofevents, that the region of interest indicates that the event has yet tooccur in the video but is likely to occur in the video within apredetermined period of time; and causing, based at least in part ondetermining the region of interest indicates that the event has yet tooccur in the video but is likely to occur in the video within thepredetermined period of time, and prior to a first occurrence of theevent in the video, a second occurrence of the pre-configured action.10. The system as recited in claim 9, wherein the acts further comprise:determining inputs for a machine learning model, the inputs includingthe frame; and training the machine learning model using the inputs todetermine at least one of an object, a human face, or an activity, andwherein determining that the region of interest is associated with theevent further comprises determining that the region of interest isassociated with the event using the machine learning model.
 11. Thesystem as recited in claim 10, wherein the acts further comprise:receiving, based at least in part on causing the second occurrence ofthe pre-configured action, feedback data from a user, the feedback dataincluding at least one of user comments or an accuracy value associatedwith determining the region of interest is associated with the event;and wherein the inputs further include the feedback data.
 12. The systemas recited in claim 9, wherein the acts further comprise: determining asecond frame of the video, wherein the second frame represents a secondimage during a second time period of the video; determining, a secondregion of interest that includes a second set of pixels included in thesecond frame; and determining, based at least in part on comparing theregion of interest and the second region of interest, a frame changebetween the frame and the second frame, the frame change representing adifference between the set of pixels in the frame and the second set ofpixels in the second frame, and wherein determining the region ofinterest is associated with the event is further based at least in parton the frame change.
 13. The system as recited in claim 9, wherein theacts further comprise: receiving user information, the user informationincluding at least one of a user interest, a user age, a user location,or a user purchase; determining, based at least in part on the userinformation, an additional pre-configured action; and causing, based atleast in part on determining the region of interest is associated withthe event, a third occurrence of the additional pre-configured action.14. The system as recited in claim 9, wherein the event includes atleast one of an object, a human face, or an activity, and the actsfurther comprise: determining, based at least in part on comparing theregion of interest and the plurality of events, that a similarity valuerepresenting a similarity between the region of interest and the eventequals to or exceeds a predetermined similarity threshold, and whereindetermining the region of interest is associated with the event isfurther based at least in part on the similarity value.
 15. The systemas recited in claim 9, wherein the first indication includes at leastone of a user input, a user selection, or a user setting, and whereinthe pre-configured action includes at least one of: causing anotification to be sent; causing a short message service (SMS) messageto be sent; causing a multimedia messaging service (MMS) message to besent; causing an email message to be sent; causing at least a portion ofthe video to be stored; causing an electronic device to be powered on;causing the electronic device to be powered off; causing an increase inaudio volume associated with the electronic device; causing a decreasein the audio volume associated with the electronic device; causing theelectronic device to present the video; causing the electronic device toidentify a second electronic device; causing the electronic device to becommunicatively coupled to the second electronic device; causing asummary of at least a portion of the video to be sent; or causinganalytics associated with at least a portion of the video to be sent.16. The system as recited in claim 9, wherein analyzing the framefurther comprises: determining a bounding box associated with the frame,the bounding box including an area and configured to enclose a pluralityof pixels included in the frame; and analyzing at least a first portionof the frame using the bounding box.
 17. One or more non-transitorycomputer-readable storage media comprising one or morecomputer-executable instructions executable by one or more processors tocause a computing device to perform operations comprising: receiving afirst indication associated with an event; receiving a second indicationassociated with a pre-configured action associated with the event;determining a frame of a video, wherein the frame represents an imageduring a time period of the video; determining a region of interest thatincludes a set of pixels included in the frame; comparing the region ofinterest and a plurality of events that include the event; determining,based at least in part on comparing the region of interest and theplurality of events, that the region of interest is associated with theevent; and causing, based at least in part on determining the region ofinterest is associated with the event and prior to a first occurrence ofthe event in the video, a second occurrence of the pre-configuredaction, the pre-configured action including at least one of sending anotification to an electronic device associated with the firstindication or causing an adjustment in audio volume associated with theelectronic device, the notification indicating that the event is likelyto occur in the video.
 18. The one or more computer-readable storagemedia as recited in claim 17, the operations further comprising:determining inputs for a machine learning model, the inputs includingthe frame; and training the machine learning model using the inputs todetermine at least one of an object, a human face, or an activity, andwherein determining that the region of interest is associated with theevent further comprises determining that the region of interest isassociated with the event using the machine learning model.
 19. The oneor more computer-readable storage media as recited in claim 17, theoperations further comprising: determining a second frame of the video,wherein the second frame represents a second image during a second timeperiod of the video; determining a second region of interest thatincludes a second set of pixels included in the second frame; anddetermining, based at least in part on comparing the region of interestand the second region of interest, a frame change between the frame andthe second frame, the frame change representing a difference between theset of pixels in the frame and the second set of pixels in the secondframe, and wherein determining the region of interest is associated withthe event is further based at least in part on the frame change.
 20. Theone or more computer-readable storage media as recited in claim 17, theoperations further comprising: receiving user information, the userinformation including at least one of a user interest, a user age, auser location, or a user purchase; determining, based at least in parton the user information, an additional pre-configured action; andcausing, based at least in part on determining the region of interest isassociated with the event, a third occurrence of the additionalpre-configured action.